Present staring IR missile warning technology operates at thresholds that approach 1000 times noise equivalent irradiance (NEI) under stressing clutter conditions severely limiting the detection range. The detection thresholds must be lower for high probability of declaration, with a low false alarm rate, of low observable targets within high clutter environments. The DICE program includes three approaches for clutter rejection and improved signal to noise. One approach combines the clutter suppression capabilities of two- dimensional adaptive spatial filters with very accurate frame registration and adaptive temporal processing to meet DICE program requirements. A second approach consists of an adaptive neural maximum likelihood algorithm combined with a hyperbolic filter (HF) for clutter suppression. The HF is a 3- dimensional filter using a multi-dimensional fast Fourier transform which suppresses clutter based on relative motion to reduce the processing load of the adaptive neural algorithm. The maximum likelihood neural algorithm uses clutter models for comparison with sensor data to eliminate the IR clutter and declare targets. The third DICE approach combines dual color discrimination with non-linear morphological signal processing for single frame missile detection. The algorithm estimates the clutter background of one IR band by using a second IR band then enhances point like geometries for missile detection. The technique requirements include probability of declaring a threat of greater than 90%, a false alarm rate (FAR) of less than 1 per hour and declaration of threat in less than or equal to 2 seconds from the time the target first crosses the detection threshold. The goal is to maintain a detection threshold of 10 times present state-of-the-art noise equivalent irradiance (NEI) even in a highly cluttered background. Program results are available from two approaches as of March 1996.